Local FAISS MCP Server

A Model Context Protocol (MCP) server that provides local vector database functionality using FAISS for Retrieval-Augmented Generation (RAG) applications.

Features

Core Capabilities

• Local Vector Storage: Uses FAISS for efficient similarity search without external dependencies
• Document Ingestion: Automatically chunks and embeds documents for storage
• Semantic Search: Query documents using natural language with sentence embeddings
• Persistent Storage: Indexes and metadata are saved to disk
• MCP Compatible: Works with any MCP-compatible AI agent or client

v0.2.0 Highlights

• CLI Tool: local-faiss command for standalone indexing and search
• Document Formats: Native PDF/TXT/MD support, DOCX/HTML/EPUB with pandoc
• Re-ranking: Two-stage retrieve and rerank for better results
• Custom Embeddings: Choose any Hugging Face embedding model
• MCP Prompts: Built-in prompts for answer extraction and summarization

Quickstart

# Install
pip install local-faiss-mcp

# Index documents
local-faiss index document.pdf

# Search
local-faiss search "What is this document about?"

Or use with Claude Code - configure MCP client (see Configuration) and try:

Use the ingest_document tool with: ./path/to/document.pdf
Then use query_rag_store to search for: "How does FAISS perform similarity search?"

Claude will retrieve relevant document chunks from your vector store and use them to answer your question.

Installation

⚡️ Upgrading? Run pip install --upgrade local-faiss-mcp

From PyPI (Recommended)

pip install local-faiss-mcp

Optional: Extended Format Support

For DOCX, HTML, EPUB, and 40+ additional formats, install pandoc:

# macOS
brew install pandoc

# Linux
sudo apt install pandoc

# Or download from: https://pandoc.org/installing.html

Note: PDF, TXT, and MD work without pandoc.

From Source

git clone https://github.com/nonatofabio/local_faiss_mcp.git
cd local_faiss_mcp
pip install -e .

Usage

Running the Server

After installation, you can run the server in three ways:

1. Using the installed command (easiest):

local-faiss-mcp --index-dir /path/to/index/directory

2. As a Python module:

python -m local_faiss_mcp --index-dir /path/to/index/directory

3. For development/testing:

python local_faiss_mcp/server.py --index-dir /path/to/index/directory

Command-line Arguments:

• --index-dir: Directory to store FAISS index and metadata files (default: current directory)
• --embed: Hugging Face embedding model name (default: all-MiniLM-L6-v2)
• --rerank: Enable re-ranking with specified cross-encoder model (default: BAAI/bge-reranker-base)

Using a Custom Embedding Model:

# Use a larger, more accurate model
local-faiss-mcp --index-dir ./.vector_store --embed all-mpnet-base-v2

# Use a multilingual model
local-faiss-mcp --index-dir ./.vector_store --embed paraphrase-multilingual-MiniLM-L12-v2

# Use any Hugging Face sentence-transformers model
local-faiss-mcp --index-dir ./.vector_store --embed sentence-transformers/model-name

Using Re-ranking for Better Results:

Re-ranking uses a cross-encoder model to reorder FAISS results for improved relevance. This two-stage "retrieve and rerank" approach is common in production sea

... View full README on GitHub